Overview

Dataset statistics

Number of variables24
Number of observations30000
Missing cells0
Missing cells (%)0.0%
Duplicate rows35
Duplicate rows (%)0.1%
Total size in memory5.5 MiB
Average record size in memory192.0 B

Variable types

Numeric20
Categorical4

Alerts

Dataset has 35 (0.1%) duplicate rowsDuplicates
PAY_SEP is highly correlated with PAY_AUG and 2 other fieldsHigh correlation
PAY_AUG is highly correlated with PAY_SEP and 7 other fieldsHigh correlation
PAY_JUL is highly correlated with PAY_SEP and 9 other fieldsHigh correlation
PAY_JUN is highly correlated with PAY_SEP and 10 other fieldsHigh correlation
PAY_MAY is highly correlated with PAY_AUG and 8 other fieldsHigh correlation
PAY_APR is highly correlated with PAY_AUG and 8 other fieldsHigh correlation
BIL_AMT_SEP is highly correlated with PAY_AUG and 8 other fieldsHigh correlation
BIL_AMT_AUG is highly correlated with PAY_AUG and 10 other fieldsHigh correlation
BIL_AMT_JUL is highly correlated with PAY_AUG and 11 other fieldsHigh correlation
BIL_AMT_JUN is highly correlated with PAY_JUL and 13 other fieldsHigh correlation
BIL_AMT_MAY is highly correlated with PAY_JUL and 13 other fieldsHigh correlation
BIL_AMT_APR is highly correlated with PAY_JUN and 11 other fieldsHigh correlation
PAY_AMT_SEP is highly correlated with BIL_AMT_SEP and 5 other fieldsHigh correlation
PAY_AMT_AUG is highly correlated with BIL_AMT_JUL and 5 other fieldsHigh correlation
PAY_AMT_JUL is highly correlated with BIL_AMT_JUN and 7 other fieldsHigh correlation
PAY_AMT_JUN is highly correlated with BIL_AMT_JUN and 6 other fieldsHigh correlation
PAY_AMT_MAY is highly correlated with BIL_AMT_JUN and 5 other fieldsHigh correlation
PAY_AMT_APR is highly correlated with BIL_AMT_MAY and 4 other fieldsHigh correlation
PAY_SEP is highly correlated with PAY_AUG and 3 other fieldsHigh correlation
PAY_AUG is highly correlated with PAY_SEP and 4 other fieldsHigh correlation
PAY_JUL is highly correlated with PAY_SEP and 4 other fieldsHigh correlation
PAY_JUN is highly correlated with PAY_SEP and 4 other fieldsHigh correlation
PAY_MAY is highly correlated with PAY_SEP and 4 other fieldsHigh correlation
PAY_APR is highly correlated with PAY_AUG and 3 other fieldsHigh correlation
BIL_AMT_SEP is highly correlated with BIL_AMT_AUG and 4 other fieldsHigh correlation
BIL_AMT_AUG is highly correlated with BIL_AMT_SEP and 4 other fieldsHigh correlation
BIL_AMT_JUL is highly correlated with BIL_AMT_SEP and 4 other fieldsHigh correlation
BIL_AMT_JUN is highly correlated with BIL_AMT_SEP and 4 other fieldsHigh correlation
BIL_AMT_MAY is highly correlated with BIL_AMT_SEP and 4 other fieldsHigh correlation
BIL_AMT_APR is highly correlated with BIL_AMT_SEP and 4 other fieldsHigh correlation
PAY_SEP is highly correlated with PAY_AUG and 1 other fieldsHigh correlation
PAY_AUG is highly correlated with PAY_SEP and 4 other fieldsHigh correlation
PAY_JUL is highly correlated with PAY_SEP and 4 other fieldsHigh correlation
PAY_JUN is highly correlated with PAY_AUG and 3 other fieldsHigh correlation
PAY_MAY is highly correlated with PAY_AUG and 4 other fieldsHigh correlation
PAY_APR is highly correlated with PAY_AUG and 5 other fieldsHigh correlation
BIL_AMT_SEP is highly correlated with BIL_AMT_AUG and 4 other fieldsHigh correlation
BIL_AMT_AUG is highly correlated with BIL_AMT_SEP and 5 other fieldsHigh correlation
BIL_AMT_JUL is highly correlated with BIL_AMT_SEP and 5 other fieldsHigh correlation
BIL_AMT_JUN is highly correlated with PAY_MAY and 5 other fieldsHigh correlation
BIL_AMT_MAY is highly correlated with PAY_APR and 6 other fieldsHigh correlation
BIL_AMT_APR is highly correlated with PAY_APR and 6 other fieldsHigh correlation
PAY_AMT_SEP is highly correlated with BIL_AMT_AUGHigh correlation
PAY_AMT_AUG is highly correlated with BIL_AMT_JULHigh correlation
PAY_AMT_JUN is highly correlated with BIL_AMT_MAYHigh correlation
PAY_AMT_MAY is highly correlated with BIL_AMT_APRHigh correlation
LIMIT_BAL is highly correlated with BIL_AMT_SEP and 5 other fieldsHigh correlation
PAY_SEP is highly correlated with PAY_AUG and 5 other fieldsHigh correlation
PAY_AUG is highly correlated with PAY_SEP and 4 other fieldsHigh correlation
PAY_JUL is highly correlated with PAY_SEP and 4 other fieldsHigh correlation
PAY_JUN is highly correlated with PAY_SEP and 4 other fieldsHigh correlation
PAY_MAY is highly correlated with PAY_SEP and 5 other fieldsHigh correlation
PAY_APR is highly correlated with PAY_SEP and 5 other fieldsHigh correlation
BIL_AMT_SEP is highly correlated with LIMIT_BAL and 6 other fieldsHigh correlation
BIL_AMT_AUG is highly correlated with LIMIT_BAL and 6 other fieldsHigh correlation
BIL_AMT_JUL is highly correlated with BIL_AMT_SEP and 6 other fieldsHigh correlation
BIL_AMT_JUN is highly correlated with LIMIT_BAL and 6 other fieldsHigh correlation
BIL_AMT_MAY is highly correlated with LIMIT_BAL and 8 other fieldsHigh correlation
BIL_AMT_APR is highly correlated with LIMIT_BAL and 6 other fieldsHigh correlation
PAY_AMT_SEP is highly correlated with PAY_AMT_AUG and 2 other fieldsHigh correlation
PAY_AMT_AUG is highly correlated with BIL_AMT_JUL and 3 other fieldsHigh correlation
PAY_AMT_JUL is highly correlated with LIMIT_BAL and 8 other fieldsHigh correlation
PAY_AMT_JUN is highly correlated with PAY_AMT_SEP and 1 other fieldsHigh correlation
PAY_AMT_MAY is highly correlated with BIL_AMT_JUL and 1 other fieldsHigh correlation
DEFAULT is highly correlated with PAY_SEPHigh correlation
PAY_AMT_AUG is highly skewed (γ1 = 30.45381745) Skewed
PAY_SEP has 14737 (49.1%) zeros Zeros
PAY_AUG has 15730 (52.4%) zeros Zeros
PAY_JUL has 15764 (52.5%) zeros Zeros
PAY_JUN has 16455 (54.9%) zeros Zeros
PAY_MAY has 16947 (56.5%) zeros Zeros
PAY_APR has 16286 (54.3%) zeros Zeros
BIL_AMT_SEP has 2008 (6.7%) zeros Zeros
BIL_AMT_AUG has 2506 (8.4%) zeros Zeros
BIL_AMT_JUL has 2870 (9.6%) zeros Zeros
BIL_AMT_JUN has 3195 (10.7%) zeros Zeros
BIL_AMT_MAY has 3506 (11.7%) zeros Zeros
BIL_AMT_APR has 4020 (13.4%) zeros Zeros
PAY_AMT_SEP has 5249 (17.5%) zeros Zeros
PAY_AMT_AUG has 5396 (18.0%) zeros Zeros
PAY_AMT_JUL has 5968 (19.9%) zeros Zeros
PAY_AMT_JUN has 6408 (21.4%) zeros Zeros
PAY_AMT_MAY has 6703 (22.3%) zeros Zeros
PAY_AMT_APR has 7173 (23.9%) zeros Zeros

Reproduction

Analysis started2021-10-08 01:25:20.379468
Analysis finished2021-10-08 01:27:44.979947
Duration2 minutes and 24.6 seconds
Software versionpandas-profiling v3.1.0
Download configurationconfig.json

Variables

LIMIT_BAL
Real number (ℝ≥0)

HIGH CORRELATION

Distinct81
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean167484.3227
Minimum10000
Maximum1000000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2021-10-08T03:27:45.160331image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum10000
5-th percentile20000
Q150000
median140000
Q3240000
95-th percentile430000
Maximum1000000
Range990000
Interquartile range (IQR)190000

Descriptive statistics

Standard deviation129747.6616
Coefficient of variation (CV)0.7746854124
Kurtosis0.5362628964
Mean167484.3227
Median Absolute Deviation (MAD)90000
Skewness0.9928669605
Sum5024529680
Variance1.683445568 × 1010
MonotonicityNot monotonic
2021-10-08T03:27:45.536315image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
500003365
 
11.2%
200001976
 
6.6%
300001610
 
5.4%
800001567
 
5.2%
2000001528
 
5.1%
1500001110
 
3.7%
1000001048
 
3.5%
180000995
 
3.3%
360000881
 
2.9%
60000825
 
2.8%
Other values (71)15095
50.3%
ValueCountFrequency (%)
10000493
 
1.6%
160002
 
< 0.1%
200001976
6.6%
300001610
5.4%
40000230
 
0.8%
500003365
11.2%
60000825
 
2.8%
70000731
 
2.4%
800001567
5.2%
90000651
 
2.2%
ValueCountFrequency (%)
10000001
 
< 0.1%
8000002
 
< 0.1%
7800002
 
< 0.1%
7600001
 
< 0.1%
7500004
< 0.1%
7400002
 
< 0.1%
7300002
 
< 0.1%
7200003
 
< 0.1%
7100006
< 0.1%
7000008
< 0.1%

SEX
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
female
18112 
male
11888 

Length

Max length6
Median length6
Mean length5.207466667
Min length4

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfemale
2nd rowfemale
3rd rowfemale
4th rowfemale
5th rowmale

Common Values

ValueCountFrequency (%)
female18112
60.4%
male11888
39.6%

Length

2021-10-08T03:27:45.920342image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-08T03:27:46.163382image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
female18112
60.4%
male11888
39.6%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

EDUCATION
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
university
14030 
graduate school
10585 
high school
4917 
other
 
468

Length

Max length15
Median length11
Mean length11.85006667
Min length5

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowuniversity
2nd rowuniversity
3rd rowuniversity
4th rowuniversity
5th rowuniversity

Common Values

ValueCountFrequency (%)
university14030
46.8%
graduate school10585
35.3%
high school4917
 
16.4%
other468
 
1.6%

Length

2021-10-08T03:27:46.381055image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-08T03:27:46.580671image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
school15502
34.1%
university14030
30.8%
graduate10585
23.3%
high4917
 
10.8%
other468
 
1.0%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

MARRIAGE
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
2
15964 
1
13659 
3
 
323
0
 
54

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row1
5th row1

Common Values

ValueCountFrequency (%)
215964
53.2%
113659
45.5%
3323
 
1.1%
054
 
0.2%

Length

2021-10-08T03:27:46.861997image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-08T03:27:47.061979image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
215964
53.2%
113659
45.5%
3323
 
1.1%
054
 
0.2%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

AGE
Real number (ℝ≥0)

Distinct56
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.4855
Minimum21
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2021-10-08T03:27:47.346382image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile23
Q128
median34
Q341
95-th percentile53
Maximum79
Range58
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.217904068
Coefficient of variation (CV)0.2597653709
Kurtosis0.04430337824
Mean35.4855
Median Absolute Deviation (MAD)6
Skewness0.7322458688
Sum1064565
Variance84.96975541
MonotonicityNot monotonic
2021-10-08T03:27:47.674244image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
291605
 
5.3%
271477
 
4.9%
281409
 
4.7%
301395
 
4.7%
261256
 
4.2%
311217
 
4.1%
251186
 
4.0%
341162
 
3.9%
321158
 
3.9%
331146
 
3.8%
Other values (46)16989
56.6%
ValueCountFrequency (%)
2167
 
0.2%
22560
 
1.9%
23931
3.1%
241127
3.8%
251186
4.0%
261256
4.2%
271477
4.9%
281409
4.7%
291605
5.3%
301395
4.7%
ValueCountFrequency (%)
791
 
< 0.1%
753
 
< 0.1%
741
 
< 0.1%
734
 
< 0.1%
723
 
< 0.1%
713
 
< 0.1%
7010
< 0.1%
6915
0.1%
685
 
< 0.1%
6716
0.1%

PAY_SEP
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.0167
Minimum-2
Maximum8
Zeros14737
Zeros (%)49.1%
Negative8445
Negative (%)28.1%
Memory size234.5 KiB
2021-10-08T03:27:47.980195image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.123801528
Coefficient of variation (CV)-67.29350467
Kurtosis2.720715042
Mean-0.0167
Median Absolute Deviation (MAD)1
Skewness0.7319749269
Sum-501
Variance1.262929874
MonotonicityNot monotonic
2021-10-08T03:27:48.253824image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
014737
49.1%
-15686
 
19.0%
13688
 
12.3%
-22759
 
9.2%
22667
 
8.9%
3322
 
1.1%
476
 
0.3%
526
 
0.1%
819
 
0.1%
611
 
< 0.1%
ValueCountFrequency (%)
-22759
 
9.2%
-15686
 
19.0%
014737
49.1%
13688
 
12.3%
22667
 
8.9%
3322
 
1.1%
476
 
0.3%
526
 
0.1%
611
 
< 0.1%
79
 
< 0.1%
ValueCountFrequency (%)
819
 
0.1%
79
 
< 0.1%
611
 
< 0.1%
526
 
0.1%
476
 
0.3%
3322
 
1.1%
22667
 
8.9%
13688
 
12.3%
014737
49.1%
-15686
 
19.0%

PAY_AUG
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.1337666667
Minimum-2
Maximum8
Zeros15730
Zeros (%)52.4%
Negative9832
Negative (%)32.8%
Memory size234.5 KiB
2021-10-08T03:27:48.534510image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.197185973
Coefficient of variation (CV)-8.949807922
Kurtosis1.57041773
Mean-0.1337666667
Median Absolute Deviation (MAD)0
Skewness0.7905650222
Sum-4013
Variance1.433254254
MonotonicityNot monotonic
2021-10-08T03:27:48.806485image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
015730
52.4%
-16050
 
20.2%
23927
 
13.1%
-23782
 
12.6%
3326
 
1.1%
499
 
0.3%
128
 
0.1%
525
 
0.1%
720
 
0.1%
612
 
< 0.1%
ValueCountFrequency (%)
-23782
 
12.6%
-16050
 
20.2%
015730
52.4%
128
 
0.1%
23927
 
13.1%
3326
 
1.1%
499
 
0.3%
525
 
0.1%
612
 
< 0.1%
720
 
0.1%
ValueCountFrequency (%)
81
 
< 0.1%
720
 
0.1%
612
 
< 0.1%
525
 
0.1%
499
 
0.3%
3326
 
1.1%
23927
 
13.1%
128
 
0.1%
015730
52.4%
-16050
 
20.2%

PAY_JUL
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.1662
Minimum-2
Maximum8
Zeros15764
Zeros (%)52.5%
Negative10023
Negative (%)33.4%
Memory size234.5 KiB
2021-10-08T03:27:49.079263image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.196867568
Coefficient of variation (CV)-7.201369245
Kurtosis2.084435875
Mean-0.1662
Median Absolute Deviation (MAD)0
Skewness0.8406818269
Sum-4986
Variance1.432491976
MonotonicityNot monotonic
2021-10-08T03:27:49.359267image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
015764
52.5%
-15938
 
19.8%
-24085
 
13.6%
23819
 
12.7%
3240
 
0.8%
476
 
0.3%
727
 
0.1%
623
 
0.1%
521
 
0.1%
14
 
< 0.1%
ValueCountFrequency (%)
-24085
 
13.6%
-15938
 
19.8%
015764
52.5%
14
 
< 0.1%
23819
 
12.7%
3240
 
0.8%
476
 
0.3%
521
 
0.1%
623
 
0.1%
727
 
0.1%
ValueCountFrequency (%)
83
 
< 0.1%
727
 
0.1%
623
 
0.1%
521
 
0.1%
476
 
0.3%
3240
 
0.8%
23819
 
12.7%
14
 
< 0.1%
015764
52.5%
-15938
 
19.8%

PAY_JUN
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct11
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.2206666667
Minimum-2
Maximum8
Zeros16455
Zeros (%)54.9%
Negative10035
Negative (%)33.5%
Memory size234.5 KiB
2021-10-08T03:27:49.636471image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.169138622
Coefficient of variation (CV)-5.29821128
Kurtosis3.496983496
Mean-0.2206666667
Median Absolute Deviation (MAD)0
Skewness0.9996294133
Sum-6620
Variance1.366885118
MonotonicityNot monotonic
2021-10-08T03:27:49.925059image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
016455
54.9%
-15687
 
19.0%
-24348
 
14.5%
23159
 
10.5%
3180
 
0.6%
469
 
0.2%
758
 
0.2%
535
 
0.1%
65
 
< 0.1%
12
 
< 0.1%
ValueCountFrequency (%)
-24348
 
14.5%
-15687
 
19.0%
016455
54.9%
12
 
< 0.1%
23159
 
10.5%
3180
 
0.6%
469
 
0.2%
535
 
0.1%
65
 
< 0.1%
758
 
0.2%
ValueCountFrequency (%)
82
 
< 0.1%
758
 
0.2%
65
 
< 0.1%
535
 
0.1%
469
 
0.2%
3180
 
0.6%
23159
 
10.5%
12
 
< 0.1%
016455
54.9%
-15687
 
19.0%

PAY_MAY
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.2662
Minimum-2
Maximum8
Zeros16947
Zeros (%)56.5%
Negative10085
Negative (%)33.6%
Memory size234.5 KiB
2021-10-08T03:27:50.214518image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.133187406
Coefficient of variation (CV)-4.256902352
Kurtosis3.989748144
Mean-0.2662
Median Absolute Deviation (MAD)0
Skewness1.008197025
Sum-7986
Variance1.284113697
MonotonicityNot monotonic
2021-10-08T03:27:50.491223image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
016947
56.5%
-15539
 
18.5%
-24546
 
15.2%
22626
 
8.8%
3178
 
0.6%
484
 
0.3%
758
 
0.2%
517
 
0.1%
64
 
< 0.1%
81
 
< 0.1%
ValueCountFrequency (%)
-24546
 
15.2%
-15539
 
18.5%
016947
56.5%
22626
 
8.8%
3178
 
0.6%
484
 
0.3%
517
 
0.1%
64
 
< 0.1%
758
 
0.2%
81
 
< 0.1%
ValueCountFrequency (%)
81
 
< 0.1%
758
 
0.2%
64
 
< 0.1%
517
 
0.1%
484
 
0.3%
3178
 
0.6%
22626
 
8.8%
016947
56.5%
-15539
 
18.5%
-24546
 
15.2%

PAY_APR
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.2911
Minimum-2
Maximum8
Zeros16286
Zeros (%)54.3%
Negative10635
Negative (%)35.4%
Memory size234.5 KiB
2021-10-08T03:27:50.765215image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-2
5-th percentile-2
Q1-1
median0
Q30
95-th percentile2
Maximum8
Range10
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.149987626
Coefficient of variation (CV)-3.950489954
Kurtosis3.42653413
Mean-0.2911
Median Absolute Deviation (MAD)0
Skewness0.9480293916
Sum-8733
Variance1.322471539
MonotonicityNot monotonic
2021-10-08T03:27:51.041699image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
016286
54.3%
-15740
 
19.1%
-24895
 
16.3%
22766
 
9.2%
3184
 
0.6%
449
 
0.2%
746
 
0.2%
619
 
0.1%
513
 
< 0.1%
82
 
< 0.1%
ValueCountFrequency (%)
-24895
 
16.3%
-15740
 
19.1%
016286
54.3%
22766
 
9.2%
3184
 
0.6%
449
 
0.2%
513
 
< 0.1%
619
 
0.1%
746
 
0.2%
82
 
< 0.1%
ValueCountFrequency (%)
82
 
< 0.1%
746
 
0.2%
619
 
0.1%
513
 
< 0.1%
449
 
0.2%
3184
 
0.6%
22766
 
9.2%
016286
54.3%
-15740
 
19.1%
-24895
 
16.3%

BIL_AMT_SEP
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct22723
Distinct (%)75.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51223.3309
Minimum-165580
Maximum964511
Zeros2008
Zeros (%)6.7%
Negative590
Negative (%)2.0%
Memory size234.5 KiB
2021-10-08T03:27:51.361717image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-165580
5-th percentile0
Q13558.75
median22381.5
Q367091
95-th percentile201203.05
Maximum964511
Range1130091
Interquartile range (IQR)63532.25

Descriptive statistics

Standard deviation73635.86058
Coefficient of variation (CV)1.437545339
Kurtosis9.806289341
Mean51223.3309
Median Absolute Deviation (MAD)21800.5
Skewness2.663861022
Sum1536699927
Variance5422239963
MonotonicityNot monotonic
2021-10-08T03:27:51.722951image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02008
 
6.7%
390244
 
0.8%
78076
 
0.3%
32672
 
0.2%
31663
 
0.2%
250059
 
0.2%
39649
 
0.2%
240039
 
0.1%
41629
 
0.1%
50025
 
0.1%
Other values (22713)27336
91.1%
ValueCountFrequency (%)
-1655801
< 0.1%
-1549731
< 0.1%
-153081
< 0.1%
-143861
< 0.1%
-115451
< 0.1%
-106821
< 0.1%
-98021
< 0.1%
-90951
< 0.1%
-81871
< 0.1%
-74381
< 0.1%
ValueCountFrequency (%)
9645111
< 0.1%
7468141
< 0.1%
6530621
< 0.1%
6304581
< 0.1%
6266481
< 0.1%
6217491
< 0.1%
6138601
< 0.1%
6107231
< 0.1%
6085941
< 0.1%
6040191
< 0.1%

BIL_AMT_AUG
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct22346
Distinct (%)74.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49179.07517
Minimum-69777
Maximum983931
Zeros2506
Zeros (%)8.4%
Negative669
Negative (%)2.2%
Memory size234.5 KiB
2021-10-08T03:27:52.118516image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-69777
5-th percentile0
Q12984.75
median21200
Q364006.25
95-th percentile194792.2
Maximum983931
Range1053708
Interquartile range (IQR)61021.5

Descriptive statistics

Standard deviation71173.76878
Coefficient of variation (CV)1.447236829
Kurtosis10.30294592
Mean49179.07517
Median Absolute Deviation (MAD)20810
Skewness2.705220853
Sum1475372255
Variance5065705363
MonotonicityNot monotonic
2021-10-08T03:27:52.503280image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02506
 
8.4%
390231
 
0.8%
32675
 
0.2%
78075
 
0.2%
31672
 
0.2%
39651
 
0.2%
250051
 
0.2%
240042
 
0.1%
-20029
 
0.1%
41628
 
0.1%
Other values (22336)26840
89.5%
ValueCountFrequency (%)
-697771
< 0.1%
-675261
< 0.1%
-333501
< 0.1%
-300001
< 0.1%
-262141
< 0.1%
-247041
< 0.1%
-247021
< 0.1%
-229601
< 0.1%
-186181
< 0.1%
-180881
< 0.1%
ValueCountFrequency (%)
9839311
< 0.1%
7439701
< 0.1%
6715631
< 0.1%
6467701
< 0.1%
6244751
< 0.1%
6059431
< 0.1%
5977931
< 0.1%
5868251
< 0.1%
5817751
< 0.1%
5776811
< 0.1%

BIL_AMT_JUL
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct22026
Distinct (%)73.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47013.1548
Minimum-157264
Maximum1664089
Zeros2870
Zeros (%)9.6%
Negative655
Negative (%)2.2%
Memory size234.5 KiB
2021-10-08T03:27:52.863260image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-157264
5-th percentile0
Q12666.25
median20088.5
Q360164.75
95-th percentile187821.05
Maximum1664089
Range1821353
Interquartile range (IQR)57498.5

Descriptive statistics

Standard deviation69349.38743
Coefficient of variation (CV)1.475106015
Kurtosis19.78325514
Mean47013.1548
Median Absolute Deviation (MAD)19708.5
Skewness3.087830046
Sum1410394644
Variance4809337537
MonotonicityNot monotonic
2021-10-08T03:27:53.237451image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
02870
 
9.6%
390275
 
0.9%
78074
 
0.2%
32663
 
0.2%
31662
 
0.2%
39648
 
0.2%
250040
 
0.1%
240039
 
0.1%
41629
 
0.1%
20027
 
0.1%
Other values (22016)26473
88.2%
ValueCountFrequency (%)
-1572641
< 0.1%
-615061
< 0.1%
-461271
< 0.1%
-340411
< 0.1%
-254431
< 0.1%
-247021
< 0.1%
-203201
< 0.1%
-177061
< 0.1%
-159101
< 0.1%
-156411
< 0.1%
ValueCountFrequency (%)
16640891
< 0.1%
8550861
< 0.1%
6931311
< 0.1%
6896431
< 0.1%
6896271
< 0.1%
6320411
< 0.1%
5974151
< 0.1%
5789711
< 0.1%
5779571
< 0.1%
5770151
< 0.1%

BIL_AMT_JUN
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct21548
Distinct (%)71.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43262.94897
Minimum-170000
Maximum891586
Zeros3195
Zeros (%)10.7%
Negative675
Negative (%)2.2%
Memory size234.5 KiB
2021-10-08T03:27:53.642901image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-170000
5-th percentile0
Q12326.75
median19052
Q354506
95-th percentile174333.35
Maximum891586
Range1061586
Interquartile range (IQR)52179.25

Descriptive statistics

Standard deviation64332.85613
Coefficient of variation (CV)1.487019671
Kurtosis11.30932483
Mean43262.94897
Median Absolute Deviation (MAD)18656
Skewness2.821965291
Sum1297888469
Variance4138716378
MonotonicityNot monotonic
2021-10-08T03:27:54.024882image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03195
 
10.7%
390246
 
0.8%
780101
 
0.3%
31668
 
0.2%
32662
 
0.2%
39644
 
0.1%
240039
 
0.1%
15039
 
0.1%
250034
 
0.1%
41633
 
0.1%
Other values (21538)26139
87.1%
ValueCountFrequency (%)
-1700001
< 0.1%
-813341
< 0.1%
-651671
< 0.1%
-506161
< 0.1%
-466271
< 0.1%
-345031
< 0.1%
-274901
< 0.1%
-243031
< 0.1%
-221081
< 0.1%
-203201
< 0.1%
ValueCountFrequency (%)
8915861
< 0.1%
7068641
< 0.1%
6286991
< 0.1%
6168361
< 0.1%
5728051
< 0.1%
5690341
< 0.1%
5656691
< 0.1%
5635431
< 0.1%
5480201
< 0.1%
5426531
< 0.1%

BIL_AMT_MAY
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct21010
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40311.40097
Minimum-81334
Maximum927171
Zeros3506
Zeros (%)11.7%
Negative655
Negative (%)2.2%
Memory size234.5 KiB
2021-10-08T03:27:54.385420image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-81334
5-th percentile0
Q11763
median18104.5
Q350190.5
95-th percentile165794.3
Maximum927171
Range1008505
Interquartile range (IQR)48427.5

Descriptive statistics

Standard deviation60797.15577
Coefficient of variation (CV)1.508187617
Kurtosis12.30588129
Mean40311.40097
Median Absolute Deviation (MAD)17688.5
Skewness2.876379867
Sum1209342029
Variance3696294150
MonotonicityNot monotonic
2021-10-08T03:27:54.729199image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03506
 
11.7%
390235
 
0.8%
78094
 
0.3%
31679
 
0.3%
32662
 
0.2%
15058
 
0.2%
39647
 
0.2%
240039
 
0.1%
250037
 
0.1%
41636
 
0.1%
Other values (21000)25807
86.0%
ValueCountFrequency (%)
-813341
< 0.1%
-613721
< 0.1%
-530071
< 0.1%
-466271
< 0.1%
-375941
< 0.1%
-361561
< 0.1%
-304811
< 0.1%
-283351
< 0.1%
-230031
< 0.1%
-207531
< 0.1%
ValueCountFrequency (%)
9271711
< 0.1%
8235401
< 0.1%
5870671
< 0.1%
5517021
< 0.1%
5478801
< 0.1%
5306721
< 0.1%
5243151
< 0.1%
5161391
< 0.1%
5141141
< 0.1%
5082131
< 0.1%

BIL_AMT_APR
Real number (ℝ)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct20604
Distinct (%)68.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38871.7604
Minimum-339603
Maximum961664
Zeros4020
Zeros (%)13.4%
Negative688
Negative (%)2.3%
Memory size234.5 KiB
2021-10-08T03:27:55.091446image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum-339603
5-th percentile0
Q11256
median17071
Q349198.25
95-th percentile161912
Maximum961664
Range1301267
Interquartile range (IQR)47942.25

Descriptive statistics

Standard deviation59554.10754
Coefficient of variation (CV)1.53206613
Kurtosis12.27070529
Mean38871.7604
Median Absolute Deviation (MAD)16755
Skewness2.846644576
Sum1166152812
Variance3546691724
MonotonicityNot monotonic
2021-10-08T03:27:55.478858image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
04020
 
13.4%
390207
 
0.7%
78086
 
0.3%
15078
 
0.3%
31677
 
0.3%
32656
 
0.2%
39645
 
0.1%
41636
 
0.1%
-1833
 
0.1%
240032
 
0.1%
Other values (20594)25330
84.4%
ValueCountFrequency (%)
-3396031
< 0.1%
-2090511
< 0.1%
-1509531
< 0.1%
-946251
< 0.1%
-738951
< 0.1%
-570601
< 0.1%
-514431
< 0.1%
-511831
< 0.1%
-466271
< 0.1%
-457341
< 0.1%
ValueCountFrequency (%)
9616641
< 0.1%
6999441
< 0.1%
5686381
< 0.1%
5277111
< 0.1%
5275661
< 0.1%
5149751
< 0.1%
5137981
< 0.1%
5119051
< 0.1%
5013701
< 0.1%
4991001
< 0.1%

PAY_AMT_SEP
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7943
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5663.5805
Minimum0
Maximum873552
Zeros5249
Zeros (%)17.5%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2021-10-08T03:27:55.875295image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11000
median2100
Q35006
95-th percentile18428.2
Maximum873552
Range873552
Interquartile range (IQR)4006

Descriptive statistics

Standard deviation16563.28035
Coefficient of variation (CV)2.924524575
Kurtosis415.2547427
Mean5663.5805
Median Absolute Deviation (MAD)1932
Skewness14.66836433
Sum169907415
Variance274342256.1
MonotonicityNot monotonic
2021-10-08T03:27:56.194266image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05249
 
17.5%
20001363
 
4.5%
3000891
 
3.0%
5000698
 
2.3%
1500507
 
1.7%
4000426
 
1.4%
10000401
 
1.3%
1000365
 
1.2%
2500298
 
1.0%
6000294
 
1.0%
Other values (7933)19508
65.0%
ValueCountFrequency (%)
05249
17.5%
19
 
< 0.1%
214
 
< 0.1%
315
 
0.1%
418
 
0.1%
512
 
< 0.1%
615
 
0.1%
79
 
< 0.1%
88
 
< 0.1%
97
 
< 0.1%
ValueCountFrequency (%)
8735521
< 0.1%
5050001
< 0.1%
4933581
< 0.1%
4239031
< 0.1%
4050161
< 0.1%
3681991
< 0.1%
3230141
< 0.1%
3048151
< 0.1%
3020001
< 0.1%
3000391
< 0.1%

PAY_AMT_AUG
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
SKEWED
ZEROS

Distinct7899
Distinct (%)26.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5921.1635
Minimum0
Maximum1684259
Zeros5396
Zeros (%)18.0%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2021-10-08T03:27:56.541181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1833
median2009
Q35000
95-th percentile19004.35
Maximum1684259
Range1684259
Interquartile range (IQR)4167

Descriptive statistics

Standard deviation23040.8704
Coefficient of variation (CV)3.891274139
Kurtosis1641.631911
Mean5921.1635
Median Absolute Deviation (MAD)1991
Skewness30.45381745
Sum177634905
Variance530881708.9
MonotonicityNot monotonic
2021-10-08T03:27:56.885983image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05396
 
18.0%
20001290
 
4.3%
3000857
 
2.9%
5000717
 
2.4%
1000594
 
2.0%
1500521
 
1.7%
4000410
 
1.4%
10000318
 
1.1%
6000283
 
0.9%
2500251
 
0.8%
Other values (7889)19363
64.5%
ValueCountFrequency (%)
05396
18.0%
115
 
0.1%
220
 
0.1%
318
 
0.1%
411
 
< 0.1%
525
 
0.1%
68
 
< 0.1%
712
 
< 0.1%
89
 
< 0.1%
96
 
< 0.1%
ValueCountFrequency (%)
16842591
< 0.1%
12270821
< 0.1%
12154711
< 0.1%
10245161
< 0.1%
5804641
< 0.1%
4155521
< 0.1%
4010031
< 0.1%
3881261
< 0.1%
3852281
< 0.1%
3849861
< 0.1%

PAY_AMT_JUL
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct7518
Distinct (%)25.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5225.6815
Minimum0
Maximum896040
Zeros5968
Zeros (%)19.9%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2021-10-08T03:27:57.274744image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1390
median1800
Q34505
95-th percentile17589.4
Maximum896040
Range896040
Interquartile range (IQR)4115

Descriptive statistics

Standard deviation17606.96147
Coefficient of variation (CV)3.36931393
Kurtosis564.3112295
Mean5225.6815
Median Absolute Deviation (MAD)1795
Skewness17.21663544
Sum156770445
Variance310005092.2
MonotonicityNot monotonic
2021-10-08T03:27:57.601080image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
05968
 
19.9%
20001285
 
4.3%
10001103
 
3.7%
3000870
 
2.9%
5000721
 
2.4%
1500490
 
1.6%
4000381
 
1.3%
10000312
 
1.0%
1200243
 
0.8%
6000241
 
0.8%
Other values (7508)18386
61.3%
ValueCountFrequency (%)
05968
19.9%
113
 
< 0.1%
219
 
0.1%
314
 
< 0.1%
415
 
0.1%
518
 
0.1%
614
 
< 0.1%
718
 
0.1%
810
 
< 0.1%
912
 
< 0.1%
ValueCountFrequency (%)
8960401
< 0.1%
8890431
< 0.1%
5082291
< 0.1%
4175881
< 0.1%
4009721
< 0.1%
3970921
< 0.1%
3804781
< 0.1%
3717181
< 0.1%
3493951
< 0.1%
3442611
< 0.1%

PAY_AMT_JUN
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6937
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4826.076867
Minimum0
Maximum621000
Zeros6408
Zeros (%)21.4%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2021-10-08T03:27:57.954620image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1296
median1500
Q34013.25
95-th percentile16014.95
Maximum621000
Range621000
Interquartile range (IQR)3717.25

Descriptive statistics

Standard deviation15666.15974
Coefficient of variation (CV)3.246147995
Kurtosis277.3337677
Mean4826.076867
Median Absolute Deviation (MAD)1500
Skewness12.90498482
Sum144782306
Variance245428561.1
MonotonicityNot monotonic
2021-10-08T03:27:58.281162image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06408
 
21.4%
10001394
 
4.6%
20001214
 
4.0%
3000887
 
3.0%
5000810
 
2.7%
1500441
 
1.5%
4000402
 
1.3%
10000341
 
1.1%
2500259
 
0.9%
500258
 
0.9%
Other values (6927)17586
58.6%
ValueCountFrequency (%)
06408
21.4%
122
 
0.1%
222
 
0.1%
313
 
< 0.1%
420
 
0.1%
512
 
< 0.1%
616
 
0.1%
711
 
< 0.1%
87
 
< 0.1%
99
 
< 0.1%
ValueCountFrequency (%)
6210001
< 0.1%
5288971
< 0.1%
4970001
< 0.1%
4321301
< 0.1%
4000461
< 0.1%
3317881
< 0.1%
3309821
< 0.1%
3200081
< 0.1%
3130941
< 0.1%
2929621
< 0.1%

PAY_AMT_MAY
Real number (ℝ≥0)

HIGH CORRELATION
HIGH CORRELATION
HIGH CORRELATION
ZEROS

Distinct6897
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4799.387633
Minimum0
Maximum426529
Zeros6703
Zeros (%)22.3%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2021-10-08T03:27:58.654777image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1252.5
median1500
Q34031.5
95-th percentile16000
Maximum426529
Range426529
Interquartile range (IQR)3779

Descriptive statistics

Standard deviation15278.30568
Coefficient of variation (CV)3.183386475
Kurtosis180.0639402
Mean4799.387633
Median Absolute Deviation (MAD)1500
Skewness11.12741705
Sum143981629
Variance233426624.4
MonotonicityNot monotonic
2021-10-08T03:27:58.980108image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06703
 
22.3%
10001340
 
4.5%
20001323
 
4.4%
3000947
 
3.2%
5000814
 
2.7%
1500426
 
1.4%
4000401
 
1.3%
10000343
 
1.1%
500250
 
0.8%
6000247
 
0.8%
Other values (6887)17206
57.4%
ValueCountFrequency (%)
06703
22.3%
121
 
0.1%
213
 
< 0.1%
313
 
< 0.1%
412
 
< 0.1%
59
 
< 0.1%
67
 
< 0.1%
79
 
< 0.1%
86
 
< 0.1%
96
 
< 0.1%
ValueCountFrequency (%)
4265291
< 0.1%
4179901
< 0.1%
3880711
< 0.1%
3792671
< 0.1%
3320001
< 0.1%
3317881
< 0.1%
3309821
< 0.1%
3268891
< 0.1%
3170771
< 0.1%
3101351
< 0.1%

PAY_AMT_APR
Real number (ℝ≥0)

HIGH CORRELATION
ZEROS

Distinct6939
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5215.502567
Minimum0
Maximum528666
Zeros7173
Zeros (%)23.9%
Negative0
Negative (%)0.0%
Memory size234.5 KiB
2021-10-08T03:27:59.310847image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1117.75
median1500
Q34000
95-th percentile17343.8
Maximum528666
Range528666
Interquartile range (IQR)3882.25

Descriptive statistics

Standard deviation17777.46578
Coefficient of variation (CV)3.408581541
Kurtosis167.1614296
Mean5215.502567
Median Absolute Deviation (MAD)1500
Skewness10.64072733
Sum156465077
Variance316038289.4
MonotonicityNot monotonic
2021-10-08T03:27:59.662832image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07173
23.9%
10001299
 
4.3%
20001295
 
4.3%
3000914
 
3.0%
5000808
 
2.7%
1500439
 
1.5%
4000411
 
1.4%
10000356
 
1.2%
500247
 
0.8%
6000220
 
0.7%
Other values (6929)16838
56.1%
ValueCountFrequency (%)
07173
23.9%
120
 
0.1%
29
 
< 0.1%
314
 
< 0.1%
412
 
< 0.1%
57
 
< 0.1%
66
 
< 0.1%
75
 
< 0.1%
86
 
< 0.1%
97
 
< 0.1%
ValueCountFrequency (%)
5286661
< 0.1%
5271431
< 0.1%
4430011
< 0.1%
4220001
< 0.1%
4035001
< 0.1%
3770001
< 0.1%
3724951
< 0.1%
3512821
< 0.1%
3452931
< 0.1%
3080001
< 0.1%

DEFAULT
Categorical

HIGH CORRELATION

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size234.5 KiB
not default
23364 
default
6636 

Length

Max length11
Median length11
Mean length10.1152
Min length7

Characters and Unicode

Total characters0
Distinct characters0
Distinct categories0 ?
Distinct scripts0 ?
Distinct blocks0 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdefault
2nd rowdefault
3rd rownot default
4th rownot default
5th rownot default

Common Values

ValueCountFrequency (%)
not default23364
77.9%
default6636
 
22.1%

Length

2021-10-08T03:28:00.006853image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Histogram of lengths of the category

Pie chart

2021-10-08T03:28:00.214849image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
ValueCountFrequency (%)
default30000
56.2%
not23364
43.8%

Most occurring characters

ValueCountFrequency (%)
No values found.

Most occurring categories

ValueCountFrequency (%)
No values found.

Most frequent character per category

Most occurring scripts

ValueCountFrequency (%)
No values found.

Most frequent character per script

Most occurring blocks

ValueCountFrequency (%)
No values found.

Most frequent character per block

Interactions

2021-10-08T03:27:34.648392image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:25:32.908574image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:25:39.159149image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:25:45.386404image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:25:51.569737image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:25:57.597177image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:03.585147image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:09.739583image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:15.708822image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:22.289795image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:28.698909image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:34.887697image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:41.787435image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:48.297687image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:54.486157image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:01.316983image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:07.997664image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:14.769524image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:20.866318image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:27.830975image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:35.004044image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:25:33.239584image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:25:39.445106image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:25:45.668936image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:25:51.875375image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:25:57.887495image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:04.097060image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:10.047539image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:16.012774image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:22.626962image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:29.000019image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:35.209838image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:42.095560image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:48.589431image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:54.815546image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:01.614928image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:08.355108image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:15.081922image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:21.182966image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:28.172955image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:35.365492image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:25:33.559271image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:25:39.760114image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:25:45.965201image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:25:52.179586image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:25:58.199634image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:04.399617image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:10.349382image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:16.309410image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:22.950105image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:29.323508image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:35.547662image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:42.419808image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:48.897510image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:55.180064image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:01.915638image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:08.696615image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:15.379547image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:21.527534image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:28.518332image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:35.704098image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:25:33.868448image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:25:40.064853image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:25:46.257216image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:25:52.479687image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:25:58.485095image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:04.697043image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:10.639445image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:16.631032image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:23.265557image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:29.658901image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:35.860671image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:42.717505image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:49.179244image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:55.487531image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:02.215202image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:09.040832image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:15.668841image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:21.879143image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:28.854784image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:36.043890image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:25:34.176763image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:25:40.370435image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:25:46.548218image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:25:52.759324image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:25:58.774516image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:04.999555image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:10.929010image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:16.917179image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:23.566053image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:29.968590image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:36.179812image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:43.029263image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:49.480056image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:55.829876image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:02.527532image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:09.377540image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:15.959564image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:22.205850image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:29.196285image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:36.395934image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:25:34.475578image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:25:40.690359image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:25:46.836571image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:25:53.040254image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:25:59.069519image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:05.287735image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:11.207178image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:17.217097image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:23.880669image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:30.259623image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:36.499572image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:43.349286image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:49.799777image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:56.179262image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:02.829786image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:09.699040image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:16.240324image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:22.559451image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:29.531955image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:36.732363image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:25:34.787674image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:25:40.985279image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
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2021-10-08T03:25:56.415683image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:02.407609image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:08.567508image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:14.515434image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:21.029720image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:27.429232image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:33.679227image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:40.163249image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:47.018830image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:53.229687image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:59.949593image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:06.379394image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:13.465307image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:19.616616image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:26.400457image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:33.316700image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:41.184076image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:25:38.275441image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:25:44.469517image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:25:50.647955image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:25:56.689415image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:02.689765image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:08.854393image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:14.797687image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:21.403168image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:27.719567image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:33.967266image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:40.467411image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:47.317153image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:53.564307image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:00.290644image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:06.688274image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:13.779320image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:19.897442image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:26.742338image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:33.628702image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:41.540794image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:25:38.579505image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:25:44.784470image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:25:50.959788image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:25:57.004408image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:03.033993image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:09.161172image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:15.103403image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:21.709399image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:28.047192image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:34.289181image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:41.147279image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:47.690764image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:53.879406image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:00.649778image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:07.425417image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:14.129380image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:20.260217image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:27.126269image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:33.972711image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:41.876261image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:25:38.864972image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:25:45.079639image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:25:51.249269image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:25:57.287502image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:03.303543image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:09.445079image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:15.399421image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:21.989803image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:28.367301image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:34.569207image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:41.467063image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:47.999930image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:26:54.197442image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:00.975320image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:07.699429image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:14.448510image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:20.555098image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:27.472089image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
2021-10-08T03:27:34.309640image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Correlations

2021-10-08T03:28:00.473368image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-10-08T03:28:01.294410image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-10-08T03:28:02.109274image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-10-08T03:28:02.848924image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2021-10-08T03:28:03.268475image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2021-10-08T03:27:42.542729image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
A simple visualization of nullity by column.
2021-10-08T03:27:44.275525image/svg+xmlMatplotlib v3.3.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

First rows

LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_SEPPAY_AUGPAY_JULPAY_JUNPAY_MAYPAY_APRBIL_AMT_SEPBIL_AMT_AUGBIL_AMT_JULBIL_AMT_JUNBIL_AMT_MAYBIL_AMT_APRPAY_AMT_SEPPAY_AMT_AUGPAY_AMT_JULPAY_AMT_JUNPAY_AMT_MAYPAY_AMT_APRDEFAULT
020000femaleuniversity12422-1-1-2-23913310268900006890000default
1120000femaleuniversity226-120002268217252682327234553261010001000100002000default
290000femaleuniversity234000000292391402713559143311494815549151815001000100010005000not default
350000femaleuniversity137000000469904823349291283142895929547200020191200110010691000not default
450000maleuniversity157-10-10008617567035835209401914619131200036681100009000689679not default
550000malegraduate school2370000006440057069576081939419619200242500181565710001000800not default
6500000malegraduate school229000000367965412023445007542653483003473944550004000038000202391375013770not default
7100000femaleuniversity2230-1-100-111876380601221-159567380601058116871542not default
8140000femalehigh school1280020001128514096121081221111793371933290432100010001000not default
920000malehigh school235-2-2-2-2-1-1000013007139120001300711220not default

Last rows

LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_SEPPAY_AUGPAY_JULPAY_JUNPAY_MAYPAY_APRBIL_AMT_SEPBIL_AMT_AUGBIL_AMT_JULBIL_AMT_JUNBIL_AMT_MAYBIL_AMT_APRPAY_AMT_SEPPAY_AMT_AUGPAY_AMT_JULPAY_AMT_JUNPAY_AMT_MAYPAY_AMT_APRDEFAULT
29990140000maleuniversity1410000001383251371421391101382624967546121600070004228150520002000not default
29991210000maleuniversity134322222250025002500250025002500000000default
2999210000malehigh school143000-2-2-28802104000000200000000not default
29993100000malegraduate school2380-1-10003042142710299670626694735500420001117844000300020002000not default
2999480000maleuniversity234222222725577770879384775198260781158700035000700004000default
29995220000malehigh school1390000001889481928152083658800431237159808500200005003304750001000not default
29996150000malehigh school243-1-1-1-10016831828350289795190018373526899812900not default
2999730000maleuniversity237432-1003565335627582087820582193570022000420020003100default
2999880000malehigh school1411-1000-1-1645783797630452774118554894485900340911781926529641804default
2999950000maleuniversity146000000479294890549764365353242815313207818001430100010001000default

Duplicate rows

Most frequently occurring

LIMIT_BALSEXEDUCATIONMARRIAGEAGEPAY_SEPPAY_AUGPAY_JULPAY_JUNPAY_MAYPAY_APRBIL_AMT_SEPBIL_AMT_AUGBIL_AMT_JULBIL_AMT_JUNBIL_AMT_MAYBIL_AMT_APRPAY_AMT_SEPPAY_AMT_AUGPAY_AMT_JULPAY_AMT_JUNPAY_AMT_MAYPAY_AMT_APRDEFAULT# duplicates
020000maleuniversity224224444165016501650165016501650000000default2
150000femalegraduate school2231-2-2-2-2-2000000000000not default2
250000maleuniversity2261-2-2-2-2-2000000000000not default2
380000femalehigh school142-2-2-2-2-2-2000000000000not default2
480000femaleuniversity131-2-2-2-2-2-2000000000000not default2
580000femaleuniversity225-2-2-2-2-2-2000000000000not default2
690000femalegraduate school2311-2-2-2-2-2000000000000not default2
7100000femaleuniversity1491-2-2-2-2-2000000000000not default2
8110000femalegraduate school2311-2-2-2-2-2000000000000not default2
9140000malegraduate school2291-2-2-2-2-2000000000000not default2